Accelerating differential evolution using an adaptive local search
Accelerating differential evolution using an adaptive local search
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DOI：
10.1109/tevc.2007.895272
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发表时间：
2008-02-01
影响因子：
14.3
通讯作者：
Iba, Hitoshi
中科院分区：
文献类型：
--
作者：
Noman, Nasimul;Iba, Hitoshi
我们提出了一个基于交叉的自适应局部搜索（LS）操作，以增强标准差异进化（DE）算法的性能。合并LS启发式方法通常在设计有效的进化算法以进行全球优化方面非常有用。但是，确定可以解决各种问题的单个LS长度是一个关键问题。我们提出了一种LS技术，可以通过使用爬山启发式的启发式调整搜索长度来解决此问题。本文的重点是证明该LS方案如何改善DE的性能。通过实验广泛的基准功能，我们表明，具有自适应LS的新版本的DE具有更好的或至少与经典DE算法相当的性能。还提供了与其他LS启发式方法以及文献中其他一些著名的进化算法的比较。
We propose a crossover-based adaptive local search (LS) operation for enhancing the performance of standard differential evolution (DE) algorithm. Incorporating LS heuristics is often very useful in designing an effective evolutionary algorithm for global optimization. However, determining a single LS length that can serve for a wide range of problems is a critical issue. We present a LS technique to solve this problem by adaptively adjusting the length of the search, using a hill-climbing heuristic. The emphasis of this paper is to demonstrate how this LS scheme can improve the performance of DE. Experimenting with a wide range of benchmark functions, we show that the proposed new version of DE, with the adaptive LS, performs better, or at least comparably, to classic DE algorithm. Performance comparisons with other LS heuristics and with some other well-known evolutionary algorithms from literature are also presented.